随机量子神经网络（RQNN）在噪声图像识别中的应用
Random Quantum Neural Networks (RQNN) for Noisy Image Recognition
摘要：
经典的随机神经网络（RNN）在决策、信号处理和图像识别任务中已经证明了有效的应用。然而，它们的实现仅限于输出概率分布代替随机尖峰信号的随机行为的确定性数字系统。我们引入了一类新的监督随机量子神经网络（RQNN），它具有强大的训练策略，以更好地利用尖峰RNN的随机性。受量子信息理论和大脑神经元信息编码的时空随机尖峰特性的启发，提出的RQNN采用具有叠加态和振幅编码特性的经典-量子混合算法。我们已经广泛验证了我们提出的RQNN模型，依靠混合经典量子算法通过PennyLane量子模拟器与有限数量的\emph{qubits}。在MNIST、FashionMNIST和KMNIST数据集上的实验表明，所提出的RQNN模型的平均分类精度达到了$94.9\%$ .此外，实验结果说明了所提出的RQNN在噪声环境中的有效性和弹性，与经典的对应物（RNN），经典的尖峰神经网络（SNN）和经典的卷积神经网络（AlexNet）相比，具有增强的图像分类准确性。此外，RQNN可以处理噪声，这对于各种应用都很有用，包括NISQ设备中的计算机视觉。PyTorch代码（https：//github.com/darthsimpus/RQN）在GitHub上提供，以复制本文中报告的结果。
英文摘要：
Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation has been limited to deterministic digital systems that output probability distributions in lieu of stochastic behaviors of random spiking signals. We introduce the novel class of supervised Random Quantum Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with superposition state and amplitude encoding features, inspired by quantum information theory and the brain's spatial-temporal stochastic spiking property of neuron information encoding. We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of \emph{qubits}. Experiments on the MNIST, FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an average classification accuracy of $94.9\%$. Additionally, the experimental findings illustrate the proposed RQNN's effectiveness and resilience in noisy settings, with enhanced image classification accuracy when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise, which is useful for various applications, including computer vision in NISQ devices. The PyTorch code (https://github.com/darthsimpus/RQN) is made available on GitHub to reproduce the results reported in this manuscript.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
